EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

Enablers and barriers to effective diabetes self-management: a multi-national investigation

Objective: The study aimed to identify the common gaps in skills and self-efficacy for diabetes self-management and explore other factors which serve as enablers of, and barriers to, achieving optimal diabetes self-management. The information gathered could provide health professionals with valuable insights to achieving better health outcomes with self-management education and support for diabetes patients. Methods: International online survey and telephone interviews were conducted on adults who have type 1 or type 2 diabetes. The survey inquired about their skills and self-efficacy in diabetes self-management, while the interviews assessed other enablers of, and barriers to, diabetes self-management. Surveys were analysed using descriptive and inferential statistics. Interviews were analysed using inductive thematic analysis. Results: Survey participants (N=217) had type 1 diabetes (38.2%) or type 2 diabetes (61.8%), with a mean age of 44.56 SD 11.51 and were from 4 continents (Europe, Australia, Asia, America). Identified gaps in diabetes self-management skills included the ability to: recognize and manage the impact of stress on diabetes, exercise planning to avoid hypoglycemia and interpreting blood glucose pattern levels. Self-efficacy for healthy coping with stress and adjusting medications or food intake to reach ideal blood glucose levels were minimal. Sixteen participants were interviewed. Common enablers of diabetes self-management included: (i) the will to prevent the development of diabetes complications and (ii) the use of technological devices. Issues regarding: (i) frustration due to dynamic and chronic nature of diabetes (ii) financial constraints (iii) unrealistic expectations and (iv) work and environment-related factors limited patients’ effective self-management of diabetes. Conclusions: Educational reinforcement using technological devices such as mobile application has been highlighted as an enabler of diabetes self-management and it could be employed as an intervention to alleviate identified gaps in diabetes self-management. Furthermore, improved approaches that address financial burden, work and environment-related factors as well as diabetes distress are essential for enhancing diabetes self-management

Model-Based Analysis of User Behaviors in Medical Cyber-Physical Systems

Human operators play a critical role in various Cyber-Physical System (CPS) domains, for example, transportation, smart living, robotics, and medicine. The rapid advancement of automation technology is driving a trend towards deep human-automation cooperation in many safety-critical applications, making it important to explicitly consider user behaviors throughout the system development cycle. While past research has generated extensive knowledge and techniques for analyzing human-automation interaction, in many emerging applications, it remains an open challenge to develop quantitative models of user behaviors that can be directly incorporated into the system-level analysis. This dissertation describes methods for modeling different types of user behaviors in medical CPS and integrating the behavioral models into system analysis. We make three main contributions. First, we design a model-based analysis framework to evaluate, improve, and formally verify the robustness of generic (i.e., non-personalized) user behaviors that are typically driven by rule-based clinical protocols. We conceptualize a data-driven technique to predict safety-critical events at run-time in the presence of possible time-varying process disturbances. Second, we develop a methodology to systematically identify behavior variables and functional relationships in healthcare applications. We build personalized behavior models and analyze population-level behavioral patterns. Third, we propose a sequential decision filtering technique by leveraging a generic parameter-invariant test to validate behavior information that may be measured through unreliable channels, which is a practical challenge in many human-in-the-loop applications. A unique strength of this validation technique is that it achieves high inter-subject consistency despite uncertain parametric variances in the physiological processes, without needing any individual-level tuning. We validate the proposed approaches by applying them to several case studies

In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as “The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention,” have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients’ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern

A group-based motivational mobile application for people with diabetes

\textit{Diabetes} is a disease defined by raised blood glucose levels, and people with the disorder have to adhere to a strict self-management regime to avoid short- and long-term complications. Today the World Health Organization reports that over 420 million people have diabetes, and the condition is one of the most common causes of death worldwide. This thesis presents the design and implementation of a group-based motivational application for people with diabetes. The proposed application applies persuasive design techniques and aspects of motivational theory to help people stay motivated in their management of the disorder through groups. Thus, decreasing the risk for short- and long-term complications. The result is \textit{Salutem}, an application where users can define custom goals, participate in groups, and receive progression updates and notifications that encourage them to motivate each other. The thesis also shows how other applications can arise from openness with user data. For example, with other application's integration to modern technologies such as HealthKit, the proposed application gathers blood glucose data from the user's CGM through HealthKit. Because of the ongoing pandemic, an extensive user study of the implemented system is not conducted. As a result, it could not be determined if the application will affect the user's motivation. However, a user study of the application's usability is conducted on a small group of users who reported that the application had an appealing design and work as intended

Supporting Diabetes Self-Management with Ubiquitous Computing Technologies: A User-Centered Inquiry

Ubiquitous computing technologies offer opportunities to improve treatments for chronic health conditions. Type 1 diabetes is a compelling use-case for such approaches, given its severity, and need for individuals to make frequent care decisions, informed by complex data. However, current apps, typically based on effortful reflection on collected data, generally show poor adoption, lack vital cognitive and emotional support, and are poorly tailored to users’ actual diabetes decision making processes. This thesis investigates how diabetes apps can be improved from a user-centered perspective. An initial questionnaire-based study investigated how well existing diabetes apps meet user needs. Perceived benefits, limitations, and reasons for low adoption rates were identified. A talk-aloud study of detailed user interactions with diabetes logging apps was conducted to characterize the benefits and limitations of diverse UI elements for T1 diabetes management, and to more precisely identify wider problems with current interaction designs. This led to positing a refined version of Mamykina et al.’s model for diabetes self-management, to account for observed practices, whereby the previously accepted habitual and sensemaking cognitive states are augmented by a posited ‘fluid contextual reasoning’ (FCR) mode, which allows multiple contextual factors to be balanced for dynamic course correction when navigating complex situations, using previously learned knowledge. To investigate user perceptions of the levels and kinds of monitoring anticipated in next generation diabetes decision support systems, a 4-week technology probe, in which participants used multiple networked devices and external data aggregation, was used to frame requirements for user-centered development of such future systems. Integrating all of the above work, an iterative design process was undertaken to create DUETS, a card-based system to facilitate reflection by designers, users, and other stakeholders on diabetes support management systems. The resulting tool and method were then implemented and evaluated through structured sessions with stakeholder focus groups

Associations between Physical Activity, Health-Related Quality of Life, Regimen Adherence, and Glycemic Control in Jordanian Adolescents with Type 1 Diabetes

Background: Adolescents with Type 1 Diabetes (T1D) display a greater than two-fold higher risk of developing microvascular and macrovascular complications compared with the non-diabetic population and the risk increases markedly as glycated hemoglobin (HbA1c) increases. The majority of the findings on the associated factors with improved glycemic control are geared toward Western population with a clear lack of studies on Middle Eastern populations. Purpose: This study aimed to examine the effect of Physical Activity (PA), Health-Related Quality of Life (HRQoL), and regimen adherence on glycemic control in Jordanian adolescents with T1D. Methods: The study utilized a cross-sectional design. Jordanian adolescents (aged 12-18) with T1D (n=74) were recruited. Self-reported measures were used including the Pediatric Quality of Life-Diabetes Module, the International Physical Activity Questionnaire, and the Summary of Diabetes Self-Care Activities. HbA1c values were obtained from the medical records. Correlation analyses were conducted using Pearson’s and Spearman’s correlation tests. Multiple regression analyses were conducted to determine if HRQoL, PA, and regimen adherence predict glycemic control. Results: Only 14.8% of the participants demonstrated good glycemic control (HbA1c ≤7.5%). Participants with poor control had a statistically significant lower mean PA of MET-minutes/week (3531.9 ± 1356.75 vs. 1619.81 ± 1481.95, p \u3c .001) compared to those with good control. The total sample was found to demonstrate low HRQoL (47.70 ± 10.32). Participants were within the acceptable range of PA (1885.38 ±1601.13) MET-minutes/week. HbA1c significantly inversely correlated with PA (r = -.328, p= .010) and regimen adherence (r= -.299, p= .018). Regimen adherence and PA significantly predicted HbA1c in the unadjusted regression model (β= -.367, p\u3c .01; β= -.409, p\u3c .01) and after adjustment for age and disease duration (β = -.360, p\u3c .01; β= -.475, p\u3c .01). In the interaction model, the interaction between PA and regimen adherence was statistically significant (β= -.304, p\u3c .05). Conclusion: Better glycemic control was significantly predicted by higher PA and regimen adherence levels. There was no significant association between glycemic control and HRQoL. Further research is needed to provide more information on psychosocial and cultural factors that impact glycemic control and quality of life in this population